{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Action1_Dolphin_Embedding\n",
    "\n",
    "数据集是 D.Lusseau 等人使用长达 7  年的时间观察新西兰 Doubtful Sound海峡 62 只海豚群体的交流情况而得到的海豚社会关系网络。这个网络具有 62 个节点，159  条边。节点表示海豚，边表示海豚间的频繁接触     数据文件：dolphins.gml     1）对Dolphin 关系进行Graph Embedding，使用GCN     2）对Embedding进行可视化（使用PCA呈现在二维平面上）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "from sklearn.decomposition import PCA\n",
    "from node2vec import Node2Vec\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'networkx.classes.graph.Graph'>\n",
      "62\n"
     ]
    }
   ],
   "source": [
    "# 数据加载，构造图\n",
    "G = nx.read_gml('dolphins.gml')\n",
    "print(type(G))\n",
    "#G = nx.read_gml('football.gml', relabel=True)\n",
    "print(len(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing transition probabilities: 100%|████████████████████████████████████████████| 62/62 [00:00<00:00, 3083.57it/s]\n",
      "Generating walks (CPU: 1): 100%|█████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 24.03it/s]\n"
     ]
    }
   ],
   "source": [
    "# 初始化Node2Vec模型\n",
    "#model = DeepWalk(G, walk_length=10, num_walks=5, workers=1)\n",
    "model = Node2Vec(G, walk_length = 10, num_walks = 5, p = 0.25, q = 4, workers = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型训练\n",
    "result = model.fit(window=4, iter=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('TSN83', 0.9995694756507874), ('SN4', 0.9974856972694397), ('Stripes', 0.997289776802063), ('Bumper', 0.9972541332244873), ('Scabs', 0.9968600273132324), ('Whitetip', 0.9968101978302002), ('Hook', 0.9968004822731018), ('Fork', 0.9966400861740112), ('SN9', 0.9964715242385864), ('SN63', 0.9964211583137512)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Zipfel': <gensim.models.keyedvectors.Vocab at 0x13e83799910>,\n",
       " 'SN4': <gensim.models.keyedvectors.Vocab at 0x13e83799970>,\n",
       " 'Bumper': <gensim.models.keyedvectors.Vocab at 0x13e837999d0>,\n",
       " 'SN96': <gensim.models.keyedvectors.Vocab at 0x13e83799a30>,\n",
       " 'PL': <gensim.models.keyedvectors.Vocab at 0x13e83799a90>,\n",
       " 'SN100': <gensim.models.keyedvectors.Vocab at 0x13e83799af0>,\n",
       " 'Hook': <gensim.models.keyedvectors.Vocab at 0x13e83799b50>,\n",
       " 'MN105': <gensim.models.keyedvectors.Vocab at 0x13e83799bb0>,\n",
       " 'Topless': <gensim.models.keyedvectors.Vocab at 0x13e83799c10>,\n",
       " 'Trigger': <gensim.models.keyedvectors.Vocab at 0x13e83799c40>,\n",
       " 'Patchback': <gensim.models.keyedvectors.Vocab at 0x13e83799ca0>,\n",
       " 'TR77': <gensim.models.keyedvectors.Vocab at 0x13e83799cd0>,\n",
       " 'Oscar': <gensim.models.keyedvectors.Vocab at 0x13e83799d30>,\n",
       " 'Kringel': <gensim.models.keyedvectors.Vocab at 0x13e83799100>,\n",
       " 'Double': <gensim.models.keyedvectors.Vocab at 0x13e83799dc0>,\n",
       " 'Beescratch': <gensim.models.keyedvectors.Vocab at 0x13e83799e20>,\n",
       " 'SN90': <gensim.models.keyedvectors.Vocab at 0x13e83799e80>,\n",
       " 'Jet': <gensim.models.keyedvectors.Vocab at 0x13e83799ee0>,\n",
       " 'Mus': <gensim.models.keyedvectors.Vocab at 0x13e83799f40>,\n",
       " 'Gallatin': <gensim.models.keyedvectors.Vocab at 0x13e83799fa0>,\n",
       " 'TR88': <gensim.models.keyedvectors.Vocab at 0x13e8374f250>,\n",
       " 'Shmuddel': <gensim.models.keyedvectors.Vocab at 0x13e8374f1f0>,\n",
       " 'Thumper': <gensim.models.keyedvectors.Vocab at 0x13e8374f550>,\n",
       " 'DN16': <gensim.models.keyedvectors.Vocab at 0x13e8374f610>,\n",
       " 'Web': <gensim.models.keyedvectors.Vocab at 0x13e8374f8b0>,\n",
       " 'Upbang': <gensim.models.keyedvectors.Vocab at 0x13e8374f670>,\n",
       " 'DN63': <gensim.models.keyedvectors.Vocab at 0x13e8374f0a0>,\n",
       " 'Knit': <gensim.models.keyedvectors.Vocab at 0x13e8374f3d0>,\n",
       " 'Ripplefluke': <gensim.models.keyedvectors.Vocab at 0x13e8374f280>,\n",
       " 'Feather': <gensim.models.keyedvectors.Vocab at 0x13e8374f940>,\n",
       " 'Zap': <gensim.models.keyedvectors.Vocab at 0x13e8374f370>,\n",
       " 'Jonah': <gensim.models.keyedvectors.Vocab at 0x13e8374fd00>,\n",
       " 'Notch': <gensim.models.keyedvectors.Vocab at 0x13e8374f2e0>,\n",
       " 'Number1': <gensim.models.keyedvectors.Vocab at 0x13e8374f340>,\n",
       " 'Stripes': <gensim.models.keyedvectors.Vocab at 0x13e837d7070>,\n",
       " 'TR120': <gensim.models.keyedvectors.Vocab at 0x13e837d70d0>,\n",
       " 'Grin': <gensim.models.keyedvectors.Vocab at 0x13e837d7130>,\n",
       " 'Haecksel': <gensim.models.keyedvectors.Vocab at 0x13e837d7190>,\n",
       " 'MN83': <gensim.models.keyedvectors.Vocab at 0x13e837d71f0>,\n",
       " 'TSN83': <gensim.models.keyedvectors.Vocab at 0x13e837d7250>,\n",
       " 'Beak': <gensim.models.keyedvectors.Vocab at 0x13e837d72b0>,\n",
       " 'TSN103': <gensim.models.keyedvectors.Vocab at 0x13e837d7310>,\n",
       " 'SN9': <gensim.models.keyedvectors.Vocab at 0x13e837d7370>,\n",
       " 'Scabs': <gensim.models.keyedvectors.Vocab at 0x13e837d73d0>,\n",
       " 'Whitetip': <gensim.models.keyedvectors.Vocab at 0x13e837d7430>,\n",
       " 'SN63': <gensim.models.keyedvectors.Vocab at 0x13e837d7490>,\n",
       " 'Cross': <gensim.models.keyedvectors.Vocab at 0x13e837d74f0>,\n",
       " 'Quasi': <gensim.models.keyedvectors.Vocab at 0x13e837d7550>,\n",
       " 'SMN5': <gensim.models.keyedvectors.Vocab at 0x13e837d75b0>,\n",
       " 'TR99': <gensim.models.keyedvectors.Vocab at 0x13e837d7610>,\n",
       " 'CCL': <gensim.models.keyedvectors.Vocab at 0x13e837d7670>,\n",
       " 'Wave': <gensim.models.keyedvectors.Vocab at 0x13e837d76d0>,\n",
       " 'DN21': <gensim.models.keyedvectors.Vocab at 0x13e837d7730>,\n",
       " 'MN60': <gensim.models.keyedvectors.Vocab at 0x13e837d7790>,\n",
       " 'Fish': <gensim.models.keyedvectors.Vocab at 0x13e837d77f0>,\n",
       " 'TR82': <gensim.models.keyedvectors.Vocab at 0x13e837d7850>,\n",
       " 'Fork': <gensim.models.keyedvectors.Vocab at 0x13e837d78b0>,\n",
       " 'SN89': <gensim.models.keyedvectors.Vocab at 0x13e837d7910>,\n",
       " 'Vau': <gensim.models.keyedvectors.Vocab at 0x13e837d7970>,\n",
       " 'MN23': <gensim.models.keyedvectors.Vocab at 0x13e837d79d0>,\n",
       " 'Five': <gensim.models.keyedvectors.Vocab at 0x13e837d7a30>,\n",
       " 'Zig': <gensim.models.keyedvectors.Vocab at 0x13e837d7a90>}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到节点的embedding\n",
    "print(result.wv.most_similar('Zipfel')) # 相似度计算\n",
    "embeddings = result.wv\n",
    "embeddings.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings.vocab['Zig'].count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在二维空间中绘制所选节点的向量\n",
    "def plot_nodes(word_list):\n",
    "    # 每个节点的embedding为100维\n",
    "    X = []\n",
    "    for item in word_list:\n",
    "        X.append(embeddings[item])\n",
    "    #print(X.shape)\n",
    "    # 将100维向量减少到2维\n",
    "    pca = PCA(n_components=2)\n",
    "    result = pca.fit_transform(X) \n",
    "    #print(result)\n",
    "    # 绘制节点向量\n",
    "    plt.figure(figsize=(12,9))\n",
    "    # 创建一个散点图的投影\n",
    "    plt.scatter(result[:, 0], result[:, 1])\n",
    "    for i, word in enumerate(list(word_list)):\n",
    "        plt.annotate(word, xy=(result[i, 0], result[i, 1]))        \n",
    "    plt.show()\n",
    "    \n",
    "plot_nodes(result.wv.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用简单numpy实现GCN\n",
    "import networkx as nx\n",
    "from networkx import to_numpy_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 对网络G进行可视化\n",
    "def plot_graph(G):\n",
    "    plt.figure()\n",
    "    pos = nx.spring_layout(G)\n",
    "    edges = G.edges()\n",
    "    nx.draw_networkx(G, pos, edges=edges);\n",
    "    nx.draw_networkx_nodes(G, pos, nodelist=G.nodes(), node_size=300, node_color='r', alpha=0.8)\n",
    "    nx.draw_networkx_edges(G, pos, edgelist=edges,alpha =0.4)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Beak', 'Beescratch', 'Bumper', 'CCL', 'Cross', 'DN16', 'DN21', 'DN63', 'Double', 'Feather', 'Fish', 'Five', 'Fork', 'Gallatin', 'Grin', 'Haecksel', 'Hook', 'Jet', 'Jonah', 'Knit', 'Kringel', 'MN105', 'MN23', 'MN60', 'MN83', 'Mus', 'Notch', 'Number1', 'Oscar', 'Patchback', 'PL', 'Quasi', 'Ripplefluke', 'Scabs', 'Shmuddel', 'SMN5', 'SN100', 'SN4', 'SN63', 'SN89', 'SN9', 'SN90', 'SN96', 'Stripes', 'Thumper', 'Topless', 'TR120', 'TR77', 'TR82', 'TR88', 'TR99', 'Trigger', 'TSN103', 'TSN83', 'Upbang', 'Vau', 'Wave', 'Web', 'Whitetip', 'Zap', 'Zig', 'Zipfel']\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "# 可视化,\n",
    "plot_graph(G)\n",
    "print(G.nodes())\n",
    "print(G.nodes['TR88']) # 无权图没有value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Beak', 'Beescratch', 'Bumper', 'CCL', 'Cross', 'DN16', 'DN21', 'DN63', 'Double', 'Feather', 'Fish', 'Five', 'Fork', 'Gallatin', 'Grin', 'Haecksel', 'Hook', 'Jet', 'Jonah', 'Knit', 'Kringel', 'MN105', 'MN23', 'MN60', 'MN83', 'Mus', 'Notch', 'Number1', 'Oscar', 'PL', 'Patchback', 'Quasi', 'Ripplefluke', 'SMN5', 'SN100', 'SN4', 'SN63', 'SN89', 'SN9', 'SN90', 'SN96', 'Scabs', 'Shmuddel', 'Stripes', 'TR120', 'TR77', 'TR82', 'TR88', 'TR99', 'TSN103', 'TSN83', 'Thumper', 'Topless', 'Trigger', 'Upbang', 'Vau', 'Wave', 'Web', 'Whitetip', 'Zap', 'Zig', 'Zipfel']\n"
     ]
    }
   ],
   "source": [
    "# 构建GCN，计算A_hat和D_hat矩阵\n",
    "order = sorted(list(G.nodes()))\n",
    "#按照字母顺序排序\n",
    "print(order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A=\n",
      " [[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "#邻接矩阵\n",
    "A = to_numpy_matrix(G, nodelist=order)\n",
    "print('A=\\n', A) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A_hat=\n",
      " [[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 1.]\n",
      " ...\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 1. ... 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# 生成对角矩阵\n",
    "I = np.eye(G.number_of_nodes())\n",
    "A_hat = A + I\n",
    "print('A_hat=\\n', A_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D_hat=\n",
      " [ 7.  9.  5.  4.  2.  5.  7.  6.  7.  8.  6.  2.  2.  9. 13.  8.  7. 10.\n",
      "  8.  5. 10.  7.  2.  4.  7.  4.  4.  6.  6.  6. 10.  2.  4.  2.  8. 12.\n",
      "  9.  3.  9.  6.  7. 11.  6.  8.  3.  7.  2.  3.  8.  5.  3.  5. 12. 11.\n",
      "  8.  3.  3. 10.  2.  6.  2.  4.]\n",
      "D_hat=\n",
      " [[7. 0. 0. ... 0. 0. 0.]\n",
      " [0. 9. 0. ... 0. 0. 0.]\n",
      " [0. 0. 5. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 6. 0. 0.]\n",
      " [0. 0. 0. ... 0. 2. 0.]\n",
      " [0. 0. 0. ... 0. 0. 4.]]\n"
     ]
    }
   ],
   "source": [
    "# D_hat为A_hat的度矩阵\n",
    "D_hat = np.array(np.sum(A_hat, axis=0))[0]\n",
    "print('D_hat=\\n', D_hat)\n",
    "# 得到对角线上的元素\n",
    "D_hat = np.matrix(np.diag(D_hat))\n",
    "print('D_hat=\\n', D_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W_1=\n",
      " [[-2.98237173e-01 -4.21179724e-01 -2.34190168e-01 -1.17576559e+00]\n",
      " [-1.24060559e+00  1.05334260e+00 -7.16359972e-01  1.05741132e+00]\n",
      " [ 4.57053791e-01  1.34339592e+00 -1.77504666e+00  1.75177321e-01]\n",
      " [-1.08928314e+00 -1.19138941e+00  1.44416587e+00 -1.64825273e+00]\n",
      " [ 1.40389258e-01  6.24475663e-01 -4.29966087e-01 -1.77607325e-01]\n",
      " [-5.39233605e-01 -8.77691463e-02  3.63347718e-01 -9.30926223e-01]\n",
      " [-5.14916502e-01  8.67918828e-01  7.45936286e-01  4.54602725e-02]\n",
      " [ 1.92015839e-01 -1.31209522e+00  6.30956093e-01 -2.12460738e+00]\n",
      " [-8.69608211e-01  1.04185313e+00 -1.56628964e+00  7.84639866e-01]\n",
      " [ 9.13708999e-01  5.72036639e-01  1.26870694e-01  3.60133797e-01]\n",
      " [ 2.99075820e-01  8.37988814e-01  1.12634734e+00 -8.13099086e-02]\n",
      " [-1.36072610e+00  7.15051200e-01 -1.45573228e-01  1.09489533e+00]\n",
      " [-6.22850568e-01 -6.12480263e-01  6.91985203e-01  3.66099458e-01]\n",
      " [ 1.05302376e-01  1.71799069e+00 -5.76592832e-01  1.09864206e+00]\n",
      " [-8.79703037e-01  3.51473667e-01 -1.65009370e-01 -7.38613665e-01]\n",
      " [-2.32761718e-01 -1.39492504e+00 -1.16125682e+00  5.13853331e-01]\n",
      " [-2.92319831e-01  8.55627603e-02  1.63622552e+00  6.86759717e-01]\n",
      " [-2.98340569e-01  1.86900845e+00  2.17387041e-01  2.44682703e-03]\n",
      " [ 2.68567741e-01 -3.99356857e-01  2.49225791e+00 -1.49086796e-01]\n",
      " [-4.56372296e-01 -1.19098193e+00  2.34951778e+00 -2.62978905e-01]\n",
      " [-4.13772681e-02  2.76567131e-01 -3.51925527e-01 -1.50268357e+00]\n",
      " [ 1.38035962e-01 -3.00022506e-01  9.57719602e-01  1.03874124e+00]\n",
      " [ 1.99303186e-01  5.90690796e-01  1.66162859e-03 -2.61459489e-01]\n",
      " [ 1.33228089e-02  1.63240655e+00 -5.80867429e-02 -3.43782985e-01]\n",
      " [ 1.74499119e-01 -1.34705587e-01 -1.25499426e+00  6.06163378e-01]\n",
      " [-2.04979038e-01 -6.71234205e-01 -6.29370393e-01  1.86634016e-01]\n",
      " [ 8.34599248e-01 -8.37761321e-01 -1.08133842e+00 -2.30717355e+00]\n",
      " [-1.81497022e+00 -1.56282709e-01  9.47227362e-01 -7.47407790e-01]\n",
      " [ 5.17073115e-01 -3.35775471e-01 -1.18385292e+00 -1.16666568e+00]\n",
      " [-4.96552331e-01 -4.25903810e-01  3.71727170e-01 -7.89613999e-01]\n",
      " [ 1.21245255e+00 -8.84437499e-01 -4.23328663e-01 -1.36367401e+00]\n",
      " [ 5.49065366e-01 -2.14537793e+00 -9.44592889e-01 -1.21587092e+00]\n",
      " [-1.19060800e+00 -3.44639716e-01  1.12177155e+00 -3.83225749e-01]\n",
      " [ 1.92619111e+00 -4.68342387e-01  1.10026790e+00 -1.43209516e+00]\n",
      " [-2.89534603e-01 -7.28250308e-01 -6.40322039e-01 -1.05587147e+00]\n",
      " [-3.55056548e-02 -2.42373949e-01  4.19939692e-01 -7.01067365e-01]\n",
      " [-6.43064444e-01  5.47778778e-01 -2.29004231e+00 -1.31672871e+00]\n",
      " [ 1.25708293e+00  3.14770130e-01 -1.58848009e+00  1.41176565e-02]\n",
      " [ 5.18107185e-01 -6.25743526e-02  8.50103782e-03 -1.13776543e+00]\n",
      " [-2.03109805e+00 -2.82532263e-01 -5.42462185e-01  9.65608052e-01]\n",
      " [ 2.73290180e-01 -4.43222010e-01  1.59206093e-03  5.19201941e-01]\n",
      " [ 1.22643387e+00  3.59112047e-01 -1.27219811e+00  3.60343434e-02]\n",
      " [ 3.74167748e-01 -9.97611049e-01 -6.49846947e-01  1.76628229e+00]\n",
      " [ 2.70412525e-01  1.02276804e+00 -4.55395398e-01 -2.82400979e+00]\n",
      " [ 5.81092873e-01 -3.09529857e-01  3.35870565e-02  4.40364541e-02]\n",
      " [ 5.47119659e-02  3.63217276e-01  1.46987933e+00  1.35023620e+00]\n",
      " [-1.81540310e+00 -4.37747107e-01  1.06246413e+00  2.47324125e-01]\n",
      " [ 2.55269242e-02 -7.02397709e-01  3.97947974e-02 -2.23720747e-01]\n",
      " [-1.02734960e+00 -1.09987096e+00  2.92847440e-01 -5.40827676e-01]\n",
      " [-1.51531507e+00 -7.50921590e-01 -1.58141501e+00  1.44098858e+00]\n",
      " [ 2.95481279e-01  6.93279722e-02 -5.96778512e-01  2.93547376e-01]\n",
      " [ 9.55599606e-01  1.82666298e+00 -4.22920663e-01  1.55209366e+00]\n",
      " [-7.17681356e-01 -2.31177608e-01  5.82735500e-01 -2.12950017e-01]\n",
      " [ 1.86881915e+00  2.34706846e+00 -1.22525964e+00 -3.15373156e-01]\n",
      " [ 8.65477934e-01  2.40660749e-01 -1.60693004e-02  1.20951535e+00]\n",
      " [-1.08477346e+00  8.35827155e-01 -2.72167842e-01  1.46607984e+00]\n",
      " [ 1.22331201e-01  8.92417137e-01  1.26865995e+00 -2.24914725e-01]\n",
      " [-6.69440390e-01 -8.31208199e-01 -7.84451261e-01 -1.14952087e+00]\n",
      " [ 2.02988300e-02 -1.11629890e-01 -6.72975094e-01  1.73114058e+00]\n",
      " [ 1.04614920e+00  9.41570974e-01 -9.88441504e-01  2.76917284e-03]\n",
      " [-1.40111751e-01  1.07832969e+00  1.31328317e+00 -9.27299891e-01]\n",
      " [ 1.80462028e+00  1.31175643e+00  1.48662401e+00 -8.89398537e-01]]\n",
      "W_2=\n",
      " [[ 1.56062262 -2.0205872 ]\n",
      " [-1.54330807 -0.81930536]\n",
      " [-0.37672791 -1.38517585]\n",
      " [-1.94551111  0.16523913]]\n"
     ]
    }
   ],
   "source": [
    "# 初始化权重, normal 正态分布 loc均值 scale标准差\n",
    "W_1 = np.random.normal(loc=0, scale=1, size=(G.number_of_nodes(), 4))\n",
    "W_2 = np.random.normal(loc=0, size=(W_1.shape[1], 2))\n",
    "print('W_1=\\n', W_1)\n",
    "print('W_2=\\n', W_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output=\n",
      " [[0.021578   0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.03990249 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.14274314 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.13602434 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.10398715 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.42310584 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [1.36207264 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.01341596 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.04251559 0.        ]\n",
      " [0.05038478 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.06244685 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.01761871 0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]\n",
      " [0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# x<0时 结果=0; x>=0时，结果=x\n",
    "def relu(x):\n",
    "    return(abs(x)+x)/2\n",
    "\n",
    "# 叠加GCN层，这里只使用单位矩阵作为特征表征，即每个节点被表示为一个 one-hot 编码的类别变量\n",
    "def gcn_layer(A_hat, D_hat, X, W):\n",
    "    return relu(D_hat**-1 * A_hat * X * W)\n",
    "H_1 = gcn_layer(A_hat, D_hat, I, W_1)\n",
    "H_2 = gcn_layer(A_hat, D_hat, H_1, W_2)\n",
    "output = H_2\n",
    "print('output=\\n', output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature_representations=\n",
      " {'Beak': array([0.021578, 0.      ]), 'Beescratch': array([0., 0.]), 'Bumper': array([0., 0.]), 'CCL': array([0., 0.]), 'Cross': array([0., 0.]), 'DN16': array([0., 0.]), 'DN21': array([0., 0.]), 'DN63': array([0., 0.]), 'Double': array([0., 0.]), 'Feather': array([0., 0.]), 'Fish': array([0., 0.]), 'Five': array([0., 0.]), 'Fork': array([0.03990249, 0.        ]), 'Gallatin': array([0., 0.]), 'Grin': array([0., 0.]), 'Haecksel': array([0., 0.]), 'Hook': array([0., 0.]), 'Jet': array([0., 0.]), 'Jonah': array([0.14274314, 0.        ]), 'Knit': array([0., 0.]), 'Kringel': array([0., 0.]), 'MN105': array([0.13602434, 0.        ]), 'MN23': array([0., 0.]), 'MN60': array([0., 0.]), 'MN83': array([0.10398715, 0.        ]), 'Mus': array([0., 0.]), 'Notch': array([0., 0.]), 'Number1': array([0., 0.]), 'Oscar': array([0., 0.]), 'Patchback': array([0., 0.]), 'PL': array([0.42310584, 0.        ]), 'Quasi': array([0., 0.]), 'Ripplefluke': array([0., 0.]), 'Scabs': array([1.36207264, 0.        ]), 'Shmuddel': array([0., 0.]), 'SMN5': array([0.01341596, 0.        ]), 'SN100': array([0., 0.]), 'SN4': array([0., 0.]), 'SN63': array([0., 0.]), 'SN89': array([0., 0.]), 'SN9': array([0., 0.]), 'SN90': array([0., 0.]), 'SN96': array([0., 0.]), 'Stripes': array([0.04251559, 0.        ]), 'Thumper': array([0.05038478, 0.        ]), 'Topless': array([0., 0.]), 'TR120': array([0., 0.]), 'TR77': array([0., 0.]), 'TR82': array([0., 0.]), 'TR88': array([0., 0.]), 'TR99': array([0.06244685, 0.        ]), 'Trigger': array([0., 0.]), 'TSN103': array([0.01761871, 0.        ]), 'TSN83': array([0., 0.]), 'Upbang': array([0., 0.]), 'Vau': array([0., 0.]), 'Wave': array([0., 0.]), 'Web': array([0., 0.]), 'Whitetip': array([0., 0.]), 'Zap': array([0., 0.]), 'Zig': array([0., 0.]), 'Zipfel': array([0., 0.])}\n"
     ]
    }
   ],
   "source": [
    "# 提取特征表征\n",
    "feature_representations = {}\n",
    "nodes = list(G.nodes())\n",
    "for i in range(len(nodes)):\n",
    "    feature_representations[nodes[i]] = np.array(output)[i]\n",
    "print('feature_representations=\\n', feature_representations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同节点value，绘制不同的颜色\n",
    "def getValue(value):\n",
    "    colorList = ['blue','green','purple','yellow','red','pink','orange','black','white','gray','brown','wheat']\n",
    "    return colorList[int(value)]\n",
    "# 绘制output，节点GCN embedding可视化\n",
    "def plot_node(output, title):\n",
    "    for i in range(len(nodes)):\n",
    "        node_name = nodes[i]\n",
    "        \n",
    "        plt.scatter(np.array(output)[i,0],np.array(output)[i,1] ,label=str(i),alpha=0.5,s = 250)\n",
    "        plt.text(np.array(output)[i,0],np.array(output)[i,1] ,i, horizontalalignment='center',verticalalignment='center', fontdict={'color':'black'})\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "plot_node(output, 'Graph Embedding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output(去掉relu)=\n",
      ": [[ 0.60319879  0.17214293]\n",
      " [-0.31802906  0.75941062]\n",
      " [-0.05755709 -0.87322288]\n",
      " [ 0.53358659  1.00956556]\n",
      " [-0.31767052 -1.17623751]\n",
      " [-1.14876573 -0.02856378]\n",
      " [-1.22427499  0.1042214 ]\n",
      " [ 0.75687296  0.34013465]\n",
      " [ 0.13986972  0.50087285]\n",
      " [-1.34216138  0.04701705]\n",
      " [ 0.27665103 -0.80911582]\n",
      " [-1.58398735 -0.48242287]\n",
      " [ 0.50147164  0.11595806]\n",
      " [-1.30833695  0.12497539]\n",
      " [ 0.66953962  0.44558304]\n",
      " [ 0.44080174  0.29281396]\n",
      " [ 0.35984476  0.33086265]\n",
      " [-0.52781305  0.28005562]\n",
      " [ 0.46165328 -0.42223517]\n",
      " [ 0.41067174  0.1827504 ]\n",
      " [ 0.28446848  0.24371566]\n",
      " [ 0.54468948 -0.53362772]\n",
      " [-1.24472195 -0.25061671]\n",
      " [ 0.1214519  -0.27600709]\n",
      " [ 0.51532105 -0.14178062]\n",
      " [ 0.26069063  1.01104523]\n",
      " [ 0.41957765  1.10929854]\n",
      " [ 0.39516096  0.78636648]\n",
      " [ 0.3645473   0.28855435]\n",
      " [ 0.50935837 -0.06174648]\n",
      " [ 1.37046086 -0.61750859]\n",
      " [ 0.50071361  0.42137668]\n",
      " [-1.34412105 -0.24073517]\n",
      " [ 3.56410171 -2.21595794]\n",
      " [ 0.74831109  0.5129884 ]\n",
      " [ 0.83791097 -0.01048785]\n",
      " [ 0.45429998  0.88469006]\n",
      " [ 0.64558962  0.59057435]\n",
      " [ 0.84576594  0.42645116]\n",
      " [-1.28136025  0.68008712]\n",
      " [ 0.33742764 -0.63536112]\n",
      " [ 0.71144716  0.14915091]\n",
      " [ 0.3285753   0.27510554]\n",
      " [ 1.16298274 -0.32749885]\n",
      " [ 1.69157145 -0.0866937 ]\n",
      " [ 0.27349076 -0.10488213]\n",
      " [-0.74682531  1.57964262]\n",
      " [ 0.96496125 -0.15450237]\n",
      " [ 0.22706442  0.19325028]\n",
      " [ 0.93642867  0.72331098]\n",
      " [ 1.60535154 -1.40954259]\n",
      " [-0.38587108 -0.20053556]\n",
      " [ 0.53150826 -0.24363343]\n",
      " [ 0.04104423 -0.65753173]\n",
      " [-0.73977703  0.27765487]\n",
      " [-0.63883402  0.18711048]\n",
      " [-0.94256088 -0.80258606]\n",
      " [-0.77673729  0.64409492]\n",
      " [-0.50323747  1.62565868]\n",
      " [ 0.39691791  0.51789659]\n",
      " [-1.16786036 -0.94708397]\n",
      " [ 0.59899648 -1.77938977]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 尝试去掉激活函数relu，重新运行一遍，发现效果反而更好\n",
    "def gcn_layer(A_hat, D_hat, X, W):\n",
    "    return D_hat**-1 * A_hat * X * W\n",
    "H_1 = gcn_layer(A_hat, D_hat, I, W_1)\n",
    "H_2 = gcn_layer(A_hat, D_hat, H_1, W_2)\n",
    "output = H_2\n",
    "print(\"output(去掉relu)=\\n:\",output)\n",
    "\n",
    "\n",
    "plot_node(output, 'Graph Embedding without relu')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.0 64-bit ('Bi_env': venv)",
   "language": "python",
   "name": "python38064bitbienvvenvba07af95a1bb4b078aa8134bba84dff2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
